Although many Known Geothermal Resource Areas in Oregon and Idaho were identified during the 1970s and 1980s, few were subsequently developed commercially. Because of advances in power plant design and energy conversion efficiency since the 1980s, some previously identified KGRAs may now be economically viable prospects. Unfortunately, available characterization data vary widely in accuracy, precision, and granularity, making assessments problematic. Here we suggest a procedure for comparing test areas against proven resources using Principal Component Analysis and cluster identification. The result is a low-cost tool for evaluating potential exploration targets using uncertain or incomplete data.

High-temperature reservoir thermal energy storage (HT-RTES) has the potential to become an indispensable component in achieving the goal of the net-zero carbon economy, given its capability to balance the intermittent nature of renewable energy generation. In this study, a machine-learning-assisted computational framework is presented to identify HT-RTES site with optimal performance metrics by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems.